audiovisual
trial_type Auditory/Left Auditory/Right Button Smiley Visual/Left Visual/Right
subject run
01 01 72 73 16 15 73 71
Time course (Magnetometers)
Time course (Gradiometers)
Global field power
Time course (Magnetometers)
Time course (Gradiometers)
Global field power
Time course (Magnetometers)
Time course (Gradiometers)
Global field power
Time course (Magnetometers)
Time course (Gradiometers)
Global field power
Full-epochs Decoding
Based on N=1 subjects. Each dot represents the mean cross-validation score for a single subject. The dashed line is expected chance performance.
  """
MNE Sample Data
"""
import mne

study_name = 'ds000248'
bids_root = f'/storage/store/data/{study_name}'
deriv_root = f'/storage/store2/derivatives/{study_name}/mne-bids-pipeline/'
subjects_dir = f'{bids_root}/derivatives/freesurfer/subjects'
N_JOBS = 1

subjects = ['01']
rename_events = {'Smiley': 'Emoji',
                 'Button': 'Switch'}
conditions = ['Auditory', 'Visual', 'Auditory/Left', 'Auditory/Right']
epochs_metadata_query = 'index > 0'  # Just for testing!
contrasts = [('Visual', 'Auditory'),
             ('Auditory/Right', 'Auditory/Left')]

time_frequency_conditions = ['Auditory', 'Visual']

ch_types = ['meg']
mf_reference_run = '01'
find_flat_channels_meg = True
find_noisy_channels_meg = True
use_maxwell_filter = True
process_er = False


def noise_cov(bp):
    # Use pre-stimulus period as noise source
    bp = bp.copy().update(processing='clean', suffix='epo')
    epo = mne.read_epochs(bp)
    cov = mne.compute_covariance(epo, rank='info', tmax=0)
    return cov


spatial_filter = 'ssp'
n_proj_eog = dict(n_mag=1, n_grad=1, n_eeg=1)
n_proj_ecg = dict(n_mag=1, n_grad=1, n_eeg=0)
ecg_proj_from_average = True
eog_proj_from_average = False

# bem_mri_images = 'FLASH'
# recreate_bem = True
# recreate_scalp_surface = True

report_evoked_n_time_points = 3
report_stc_n_time_points = 3


def mri_t1_path_generator(bids_path):
    # don't really do any modifications – just for testing!
    return bids_path

on_error = "debug"

  Platform:         Linux-4.15.0-136-generic-x86_64-with-glibc2.27
Python:           3.9.9 | packaged by conda-forge | (main, Dec 20 2021, 02:41:03)  [GCC 9.4.0]
Executable:       /data/parietal/store/work/agramfor/mambaforge/bin/python3.9
CPU:              x86_64: 88 cores
Memory:           503.8 GB

mne:              1.2.dev0
numpy:            1.21.6 {MKL 2022.0-Product with 1 thread}
scipy:            1.8.1
matplotlib:       3.4.3 {backend=agg}

sklearn:          0.24.2
numba:            0.55.1
nibabel:          3.2.1
nilearn:          Not found
dipy:             Not found
openmeeg:         Not found
cupy:             Not found
pandas:           1.3.3
pyvista:          0.32.1 {OpenGL 3.3 (Core Profile) Mesa 20.0.8 via llvmpipe (LLVM 10.0.0, 256 bits)}
pyvistaqt:        0.5.0
ipyvtklink:       Not found
vtk:              9.1.0
qtpy:             1.11.2 {PyQt5=5.12.9}
ipympl:           Not found
pyqtgraph:        Not found
pooch:            v1.6.0

mne_bids:         0.11.dev0
mne_nirs:         Not found
mne_features:     Not found
mne_qt_browser:   Not found
mne_connectivity: Not found
mne_icalabel:     Not found